IMPLEMENTASI METODE GREY LEVEL CO-OCCURRENCE MATRIX UNTUK MENGIDENTIFIKASI PENYAKIT PADA TANAMAN KELAPA SAWIT BERDASARKAN CITRA DAUN KELAPA SAWIT

Vesti Susilawati, Warda and Erlansari, Aan and Ernawati, Ernawati (2021) IMPLEMENTASI METODE GREY LEVEL CO-OCCURRENCE MATRIX UNTUK MENGIDENTIFIKASI PENYAKIT PADA TANAMAN KELAPA SAWIT BERDASARKAN CITRA DAUN KELAPA SAWIT. Undergraduated thesis, Universitas Bengkulu.

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Abstract

Oil palm (Elaeis guneensis Jacq.) is one of several oil-producing plants. Production costs can also increase significantly if plant diseases are not detected and cured at an early stage. Leaf spot disease, anthracnose disease, blast diases disease and yellow line are diseases that often attack oil palm leaves at an early stage in nurseries. The disease can cause complications that affect seed yields that are not good, resulting in sub-optimal oil yields. This disease must be classified according to its type to get treatment. To be classified, some information is required. These four diseases can be recognized visually because they have unique color and texture characteristics. But visual observation has some disadvantages such as subjectivity and less accuracy. Through an image, information about plant diseases can be learned, such as: texture and color. Image processing is one of the most widely used techniques for detecting and classifying plant leaf diseases. The area of interest is found by using K-Means Clustering segmentation, then extracting texture features using the Gray Level Co-occurrence Matrix Method. while the classifier uses the Euclidean distance method. The proposed research is able to identify leaf diseases in oil palm plants with an accuracy of 60%. Keyword : Implementation, oil palm Disease, Gray Level Co-Occurrence.

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